{"id":"W3119931323","doi":"10.1007/s42979-020-00403-9","title":"RGAN: Rényi Generative Adversarial Network","year":2021,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ericsson (Canada)","funders":"","keywords":"Discriminator; Generative grammar; Adversarial system; Function (biology); Stability (learning theory); Computer science; Generative adversarial network; Artificial intelligence; Machine learning; Deep learning; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008717111,0.0002718312,0.0003068954,0.0001097427,0.0009510776,0.001139189,0.002090107,0.00006839984,0.00006479543],"category_scores_gemma":[0.00008227472,0.0002517337,0.0001337885,0.002759688,0.0004197897,0.001743348,0.001786942,0.0002116489,0.000134837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001025498,"about_ca_system_score_gemma":0.0008926324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001218361,"about_ca_topic_score_gemma":0.0000165777,"domain_scores_codex":[0.9965454,0.0002120319,0.0003327903,0.00125968,0.0007676308,0.0008824742],"domain_scores_gemma":[0.9976803,0.000147203,0.0001259459,0.001142246,0.000604102,0.0003002022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000167979,0.0002750026,0.0007501793,0.00001347142,0.0001091631,0.0005573631,0.002448021,0.3764414,0.01697391,0.216103,0.07108691,0.3152248],"study_design_scores_gemma":[0.0004084153,0.0000998947,0.001484329,0.00002673817,0.000008808551,0.00007060119,0.00001208317,0.9430431,0.02984248,0.006873906,0.01765794,0.0004717578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001050834,0.0002597406,0.9869703,0.0020571,0.006162188,0.0001285904,0.00000193424,0.0001700989,0.003199224],"genre_scores_gemma":[0.3036602,0.0000237297,0.6908279,0.002470587,0.002730167,0.00000792311,0.000002790518,0.00001091756,0.0002657046],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5666016,"threshold_uncertainty_score":0.9999935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01108056440714487,"score_gpt":0.2231220541459432,"score_spread":0.2120414897387983,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}